code: def reset(self, *, seed=None, options=None):
      self.cur_obs, infos = self.flat_env.reset()
      self.current_goal = None
      self.steps_remaining_at_level = None
      self.num_high_level_steps = 0
      # current low level agent id. This must be unique for each high level
      # step since agent ids cannot be reused.
      self.low_level_agent_id = "low_level_{}".format(
          self.num_high_level_steps
      )
      return {
          "high_level_agent": self.cur_obs,
      }, {"high_level_agent": infos}
  
file_path: ray/rllib/examples/env/windy_maze_env.py
imports: import gymnasium
  from gymnasium.spaces import Box, Discrete, Tuple
  import logging
  import random
  from ray.rllib.env import MultiAgentEnv
label_desc: Use image node_id 2 for calling the HierarchicalWindyMazeEnv obj's underlying member method code with example usage: obj.reset() and returns: dict,  dict